Akash Mer
2023-07-29
OzoneXpect aims to predict mean Ozone levels
in parts per billion(ppb) based on the measurement taken or the
information known by the user.
The user is allowed to input the following information :
Data Used - The data for the app comes from the
airquality data set in R datasets package
which is as follows,
'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
27.45% of rows contain missing values.
impute.knn() function with k = 10 from the
impute package was used to impute these missing values
OzoneXpect predicts using 2 different models,
airquality data set. The data is subsetted in case a
particular month was selected to ensure stratification and avoid any
confounding due to the month variable. This model is built
using the lm() function in R.loess() function in R with a span of
0.7Then, the mean ozone level is predicted using both models and returned with a 95% prediction interval for both models
Salient Features :
Salient Features :
airquality data set to ensure higher
accuracy.| Example Prediction | ||
| Mean Ozone Level(ppb) | 95% Prediction Interval | |
|---|---|---|
| lower limit | upper limit | |
| Linear Model | ||
| 1 41.06939 | 1 -2.581093 | 1 84.71988 |
| Loess Model | ||
| 32.99557 | 30.053930 | 35.93720 |
| Reference: airquality data set from R datasets package | ||
| 1 These negative values are not helpful | ||